Digital Library of the European Council for Modelling and Simulation

Title:	Profiling And Rating Prediction From Multi-Criteria Crowd-Sourced Hotel Ratings
Authors:	Fatima Leal, Horacio Gonzalez–Velez, Benedita Malheiro, Juan Carlos Burguillo
Published in:	(2017).ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi:10.7148/2017 ISBN: 978-0-9932440-4-9/ ISBN: 978-0-9932440-5-6 (CD) 31st European Conference on Modelling and Simulation, Budapest, Hungary, May 23^rd – May 26^th, 2017
Citation format:	Fatima Leal, Horacio Gonzalez–Velez, Benedita Malheiro, Juan Carlos Burguillo (2017). Profiling And Rating Prediction From Multi-Criteria Crowd-Sourced Hotel Ratings, ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi: 10.7148/2017-0576
DOI:	https://doi.org/10.7148/2017-0576
Abstract:	Based on historical user information, collaborative filters predict for a given user the classification of unknown items, typically using a single criterion. However, a crowd typically rates tourism resources using multi-criteria, i.e., each user provides multiple ratings per item. In order to apply standard collaborative filtering, it is necessary to have a unique classification per user and item. This unique classification can be based on a single rating – single criterion (SC) profiling – or on the multiple ratings available – multicriteria (MC) profiling. Exploring both SC and MC profiling, this work proposes: ({) the selection of the most representative crowd-sourced rating; and ({{) the combination of the different user ratings per item, using the average of the non-null ratings or the personalised weighted average based on the user rating profile. Having employed matrix factorisation to predict unknown ratings, we argue that the personalised combination of multi-criteria item ratings improves the tourist profile and, consequently, the quality of the collaborative predictions. Thus, this paper contributes to a novel approach for guest profiling based on multi-criteria hotel ratings and to the prediction of hotel guest ratings based on the Alternating Least Squares algorithm. Our experiments with crowd-sourced Expedia and TripAdvisor data show that the proposed method improves the accuracy of the hotel rating predictions.
Full text: